Color image inpainting is a challenging task in imaging science. The existing method is based on real operation, and the red, green and blue channels of the color image are processed separately, ignoring the correlation between each channel. In order to make full use of the correlation between each channel, this paper proposes a Quaternion Generative Adversarial Neural Network (QGAN) model and related theory, and applies it to solve the problem of color image inpainting with large area missing. Firstly, the definition of quaternion deconvolution is given and the quaternion batch normalization is proposed. Secondly, the above two innovative modules are applied to generate adversarial networks to improve stability. Finally, QGAN is applied to color image inpainting and compared with other state-of-the-art algorithms. The experimental results show that QGAN has superiority in color image inpainting with large area missing.

翻译：彩色图像修复是成像科学中的一项具有挑战性的任务。现有方法基于实数运算，对彩色图像的红、绿、蓝通道分别进行处理，忽略了各通道间的相关性。为了充分利用各通道间的相关性，本文提出了一种四元数生成对抗神经网络模型及相关理论，并将其应用于解决大面积缺失的彩色图像修复问题。首先，给出了四元数反卷积的定义，并提出了四元数批量归一化方法。其次，将上述两个创新模块应用于生成对抗网络以提高其稳定性。最后，将QGAN应用于彩色图像修复，并与其他先进算法进行了比较。实验结果表明，QGAN在大面积缺失的彩色图像修复中具有优越性。